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Description
As an engineer on ML Compute team, your work will include:
- Drive large-scale pre-training initiatives to support cutting-edge foundation models, focusing on resiliency, efficiency, scalability, and resource optimization.
- Enhance distributed training techniques for foundation models.
- Research and implement new patterns and technologies to improve system performance, maintainability, and design.
- Optimize execution and performance of workloads built with JAX, PyTorch, XLA and CUDA on large distributed systems.
- Leverage high-performance networking technologies such as NCCL for GPU collectives and TPU interconnect (ICI/Fabric) for large-scale distributed training.
- Architect a robust MLOps platform to streamline and automate pretraining operations.
- Operationalize large-scale ML workloads on Kubernetes, ensuring distributed trainings are robust, efficient, and fault-tolerant.
- Lead complex technical projects, defining requirements and tracking progress with team members.
- Collaborate with cross-functional engineers to solve large-scale ML training challenges.
- Mentor engineers in areas of your expertise, fostering skill growth and knowledge sharing.
- Cultivate a team centered on collaboration, technical excellence, and innovation.
Minimum Qualifications
Bachelors in Computer Science, engineering, or a related field
6+ years of hands-on experience in building scalable backend systems for training and evaluation of machine learning models
Proficient in relevant programming languages, like Python or Go
Strong expertise in distributed systems, reliability and scalability, containerization, and cloud platforms
Proficient in cloud computing infrastructure and tools: Kubernetes, Ray, PySpark
Ability to clearly and concisely communicate technical and architectural problems, while working with partners to iteratively find
Preferred Qualifications
Advance degrees in Computer Science, engineering, or a related field
Proficient in working with and debugging accelerators, like: GPU, TPU, AWS Trainium
Proficient in ML training and deployment frameworks, like: JAX, Tensorflow, PyTorch, TensorRT, vLLM
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: 90733111
- Position Id: bd4106c6054a2f8e8f354856121bb905
- Posted 30+ days ago
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